Župančić, Vedran

Background The Woven EndoBridge (WEB) device represents an innovative solution for cerebral aneurysm occlusion, particularly for challenging wide-neck bifurcation aneurysms. However, factors affecting sustained occlusion remain poorly understood. We utilized machine learning to attempt to identify predictors of favorable long-term outcomes following WEB treatment. Methods In this multicenter retrospective study, we collected patient demographics, aneurysm characteristics, procedural details, and clinical outcomes. The primary endpoint was improvement in occlusion status, defined as maintained Raymond-Roy Occlusion Classification (RROC) grade 1, or improvement from grade 2 to 1, or from grade 3 to either 2 or 1 on final angiographic follow up. The dataset was split into training (75%) and validation (25%) sets. The CatBoost algorithm was selected based on performance metrics, with Shapley Additive exPlanations (SHAP) values calculated to determine feature importance. Furthermore, a multivariable binomial logistic regression model was performed to validate machine learning findings. Results Among 720 aneurysms from 36 hospitals, 84% showed improvement in occlusion at follow up. Both machine learning and multivariable logistic regression identified aneurysm height as the most consistent correlate of nonimprovement (odds ratio (OR) 0.90 per mm, p = 0.022). In the CatBoost model, the highest-ranking features by SHAP included aneurysm height, patient age, treatment acuity, ACom location, WEB-SLS device, bifurcation anatomy, aneurysm multiplicity, baseline modified Rankin Scale, access route, and partial thrombosis. Conclusions Machine-learning and regression analyses identified consistent predictors of occlusion improvement after WEB treatment, with aneurysm height most strongly linked to nonimprovement. These insights may guide patient selection and follow up. Findings require cautious interpretation and external validation in larger cohorts.

Background Endovascular treatment with the woven endobridge (WEB) device has been widely utilized for managing intracranial aneurysms. However, predicting the probability of achieving adequate occlusion (Raymond–Roy classification 1 or 2) remains challenging. Objective Our study sought to develop and validate a predictive calculator for adequate occlusion using the WEB device via data from a large multi-institutional retrospective cohort. Methods We used data from the WorldWide WEB Consortium, encompassing 356 patients from 30 centers across North America, South America, and Europe. Bivariate and multivariate regression analyses were performed on a variety of demographic and clinical factors, from which predictive factors were selected. Calibration and validation were conducted, with variance inflation factor (VIF) parameters checked for collinearity. Results A total of 356 patients were included: 124 (34.8%) were male, 108 (30.3%) were elderly (≥65 years), and 118 (33.1%) were current smokers. Mean maximum aneurysm diameter was 7.09 mm (SD 2.71), with 112 (31.5%) having a daughter sac. In the multivariate regression, increasing aneurysm neck size (OR 0.706 [95% CI: 0.535–0.929], p = 0.13) and partial aneurysm thrombosis (OR 0.135 [95% CI: 0.024–0.681], p = 0.016) were found to be the only statistically significant variables associated with poorer likelihood of achieving occlusion. The predictive calculator shows a c-statistic of 0.744. Hosmer–Lemeshow goodness-of-fit test indicated a satisfactory model fit with a p-value of 0.431. The calculator is available at: https://neurodx.shinyapps.io/WEBDEVICE/ . Conclusion The predictive calculator offers a substantial contribution to the clinical toolkit for estimating the likelihood of adequate intracranial aneurysm occlusion by WEB device embolization.